I'm using this Java machine learning library: https://sites.google.com/site/qianmingjie/home/toolkits/laml
From the library I'm using Logistic Regression: http://web.engr.illinois.edu/~mqian2/upload/projects/java/LAML/doc/ml/classification/LogisticRegression.html
This class supports 4 types of regularizations:
- 0: No regularization
- 1: L1 regularization
- 2: L2^2 regularization
- 3: L2 regularization
- 4: Infinity norm regularization
You basically create an object of Regular Regression using this code:
int regularizationType = 1; double lambda = 0.1; Classifier logReg = new LogisticRegression(regularizationType, lambda);
When I tried it I noticed this weird thing:
As far as I know the idea of regularization is to have the weights as small as possible and so using lambda will penalize large weights. So one should use a large lambda to regularize. However, when I used L1 regularization with a
lambda=1 the performance was worse than using
lambda=0.0001. Actually the best performance I got is when I used
1- How can logistic regression without regularization perform better than when using regularization? Isn't the idea of regularization after all is to make the performance better?!
2- Should I use large values for the regularization parameter?!
3- Is using regularization in general always good?